Controlling multi-stage manufacturing process based on internet of things (IoT) sensors and cognitive rule induction

ABSTRACT

Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

FIELD

The present application relates generally to computers and computerapplications, and more particularly to multi-stage manufacturing processand controlling the same.

BACKGROUND

In many manufacturing processes that involve multiple processing stepsor stages such as in steel manufacturing and chemical productsmanufacture, the quality of a product that is manufactured is onlydiscovered at the end of the manufacturing process. The product qualitysuch as the degree of defect, however, is often influenced by processingconditions during upstream or early processing stages. Despite thoseprocessing conditions which may be present during the upstreamprocessing stages, there is no easy way of uncovering early indicatorsthat could remediate the product quality problems.

Known quality control methods may apply a conventional process qualitycontrol system to a processing stage with pre-determined control bounds,which operates on each processing stage independently. An example of aconventional process quality control system is statistical processcontrol (SPC). Integrating independent operations of differentprocessing stages in identifying product quality indicators or productquality control mechanisms has been a challenge in multi-stagemanufacturing processes.

BRIEF SUMMARY

A system and method of controlling product production in multi-stagemanufacturing process may be provided. The system in one aspect mayinclude a hardware processor communicatively coupled to a storage devicestoring product genealogy data. The product genealogy data may include ahistory of processing conditions at all stages in the multi-stagemanufacturing process in manufacturing a product. The processingconditions may be expressed in terms of process variables andcorresponding values measured by sensors coupled to processing units ina manufacturing facility implementing the multi-stage manufacturingprocess. The hardware processor receives the product genealogy data, forexample, from a database storing such genealogy data. The hardwareprocessor may select a subset of process variables that influenceproduct quality, from the process variables. The hardware processor mayexecute a machine learning algorithm with the product genealogy data andthe subset of process variables as input, for the machine learningalgorithm to learn causal relationships between the processingconditions and the product quality. The hardware processor mayautomatically generate control rules based on the causal relationships,the control rules for controlling process variable set points at one ormore stages in the multi-stage manufacturing process. The hardwareprocessor may receive real time sensor data from the sensors andinstantiate one or more of the control rules based on the real timesensor data, wherein a control rule is fired responsive to the real timesensor data meeting a conditional part of the control rule. Responsiveto the control rule firing, the hardware processor controls an actuatorcoupled to one or more of the processing units to set a processingvariable to a set point specified in the control rule.

A method of controlling product production in multi-stage manufacturingprocess, in one aspect, may include receiving product genealogy datacomprising a history of processing conditions at all stages in themulti-stage manufacturing process in manufacturing a product, theprocessing conditions expressed in terms of process variables andcorresponding values measured by sensors coupled to processing units ina manufacturing facility implementing the multi-stage manufacturingprocess. The method may also include selecting a subset of processvariables that influence product quality, from the process variables.The method may further include executing a machine learning algorithmwith the product genealogy data and the subset of process variables asinput, the machine learning algorithm learning causal relationshipsbetween the processing conditions and the product quality. The methodmay further include automatically generating control rules based on thecausal relationships, the control rules for controlling process variableset points at one or more stages in the multi-stage manufacturingprocess. The method may also include receiving real time sensor datafrom the sensors. The method may further include instantiating one ormore of the control rules based on the real time sensor data, wherein acontrol rule is fired responsive to the real time sensor data meeting aconditional part of the control rule. The method may also include,responsive to the control rule firing, controlling an actuator coupledto one or more of the processing units to set a processing variable to aset point specified in the control rule.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows an example product manufacturing flow in one embodiment ofthe present disclosure.

FIG. 1B shows control rule induction process and manufacture controlprocess in one embodiment of the present disclosure.

FIG. 1C-1, FIG. 1C-2, and FIG. 1C-3 show outputs generated from controlrule learning in one embodiment of the present disclosure.

FIG. 2 is a flow diagram illustrating a method of the present disclosurein one embodiment.

FIG. 3 shows an example user interface display showing productmanufacturing process with instantiation of control rules based on realtime IoT sensor data that may actuate changing or controlling one ormore set points, in one embodiment of the present disclosure.

FIG. 4 is a diagram illustrating system components in one embodiment ofthe present disclosure.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a manufacturing process control system in oneembodiment of the present disclosure.

DETAILED DESCRIPTION

A system, method and techniques may be provided for controllingmulti-stage manufacturing process based on Internet of Things (IoT)sensors and cognitive rule induction. An aspect of the system and methodmay include selecting features of processing conditions duringproduction stages that influence product quality, for example, expressedas p1.s1.v2 (product1, stage1, variable2), p1.s2.v1 (product1, stage2,variable1), and p1.s3.v5 (product1, stage3, variable5). Another aspectof the system and method may include using genealogy data and theprocess variables (also referred as features), which are recordedprocess conditions measured (or recorded) by sensors, selected to trainor learn various tree induction algorithms (for example, decision treealgorithms such as CART (Classification and Regression Tree), ID2(Iterative Dichotomiser 2), C5.0 and Random Forest), evaluating theaccuracy of the algorithms and identifying one or more most accuratealgorithms. Yet another aspect of the system and method may includegenerating feedback rules and feed forward rules that set a controllableprocessing set point (processing condition) of a stage of amanufacturing process so that desired product quality is obtained.

The system and method in one embodiment automate learning of feedbackand feed forward control rules for IoT-based product quality control inmulti-stage manufacturing process utilizing both real time data (forexample, streaming data from all devices and/or sensors from all stagesof a manufacturing process) and historic product genealogy data.

Accessing all the process variable data from all the processing stagesin a multi-stage manufacturing process in real time and learning acausal relationship between the process variables (or processingconditions) from different stages and product quality present achallenge in manufacturing industry. Deriving a process control method(for example, including feed forward and feedback analytics) thatobtains desired product quality in real time has not been easy. IoT(Internet of Things) is a technological foundation that can provideconnectivity and real time messaging of sensor data detected by anddistributed from many sensors, devices, equipment and unit operations(stages) across a whole manufacturing or production process. The systemand method in the present disclosure in one embodiment utilize IoT (forexample, IoT sensor detected data) in developing a process qualitycontrol method for real-time product quality improvement.

The system and method described in the present disclosure in oneembodiment automatically learn feedback and feed forward control rulesthat define causal relationship between processing conditions andproduct quality, generating product quality control rules, for example,based on IoT detected real-time data (streaming data from all thedevices/sensors from all the stages of process) and historic productgenealogy data, to control product quality in a multi-stagemanufacturing process.

In one embodiment, features or process variables in production stagesthat influence the product quality are selected for induction ofcognitive rules. Genealogy data and the process variables selected areinput to a machine learning algorithm to learn a causal relationshipbetween the processing conditions of all the production stages andproduct quality. Examples of a machine learning algorithm may includebut are not limited to, decision tree algorithms such as CART(Classification and Regression Tree), ID2 (Iterative Dichotomiser 2),C5.0 and Random Forest algorithms.

In one embodiment, quality control rules (for example, feedback and feedforward rules) that set a controllable processing set point of a stage(processing condition at a stage), for different stages in a multi-stagemanufacturing process, are generated. The effectiveness of the qualitycontrol rules is learned and the most effective rules are identified.For example, n-number of rules having the highest effectiveness measuremay be identified.

In one aspect, the quality control rules are instantiated in real timeusing both real time data (streaming data from all the devices/sensorsfrom all the stages of process) and historic product genealogy data. Thecontrol action (for example, feedback or feed forward action),determined according to the identified n-number of the quality controlrules, is sent to a process controller, a process advisory system,and/or an actuator, which implements the control action. Theeffectiveness of the control actions after the process controller, theprocess advisory system, and/or the actuator implements the controlaction, is measured and stored in as product genealogy data, which thenmay be used in learning the quality control rules.

FIG. 1A, FIG. 1B, FIG. 1C-1, FIG. 1C-2, and FIG. 1C-3 illustrate amethod of the present disclosure in one embodiment. FIG. 1A shows in oneembodiment a product manufacturing flow. FIG. 1B shows control ruleinduction process and manufacture control process in one embodiment ofthe present disclosure. FIG. 1C-1, FIG. 1C-2, and FIG. 1C-3 show outputsgenerated from control rule learning in one embodiment of the presentdisclosure.

Referring to FIG. 1A, consider an example in which a multi-stagemanufacturing process includes stages S1 (102), S2 (104), S3 (106) andS4 (108) through which material passes through to become an end product.Note multi-stage manufacturing processes may have different number ofstages, for example, 1 through M. FIG. 1A shows four stages as anexample for simplicity. At each stage, the material is acted on toproduce an intermediary product. The last stage in the manufacturingprocess produces the end product. For instance, FIG. 1A shows, asexample, material for products identified as identified as Product 1,Product 2, . . . Product N) entering stage 1 (102), and is acted on withone or more operating conditions. An operating condition z for a productx at a stage y is expressed as px.sy.vz. For instance, Product 1 atStage 1 with operating condition 1 is expressed as p1.s1.v1; Product 1at Stage 1 with operating condition 2 is expressed as p1.s1.v2; Product2 at Stage 1 with operating condition 1 is expressed as p2.s1.v1.Similarly, as an example, Product 1 at Stage 2 with operating condition1 is expressed as p1.s2.v1; Product 1 at Stage 2 with operatingcondition 2 is expressed as p1.s2.v2; Product 2 at Stage 2 withoperating condition 1 is expressed as p2.s2.v1. Likewise, as an example,Product 1 at Stage 3 with operating condition 1 is expressed asp1.s3.v1; Product 1 at Stage 3 with operating condition 2 is expressedas p1.s3.v2; Product 2 at Stage 3 with operating condition 1 isexpressed as p2.s3.v1. A quality measure of a product may be expressedas p1.q1, p1.q2, . . . p1.qx, with q1 being a quality attribute. Takefor example, steel manufacturing, and steel as an example product,examples of quality attributes may include, but are not limited tosurface smoothness, content purity, carbon content, and strength ofsteel that is manufactured.

Each stage acts on the raw material or intermediary material or productthat passes through it, under its operating conditions. Operatingconditions are also referred to as processing conditions or features.Examples of operating conditions include but are not limited to,temperature, pressure, flow rate, chemical content of material.

Referring to FIG. 1A, material (e.g., raw material) for manufacturingProduct 1, for example enters Stage 1 (102) and is acted on withoperating condition 1 (expressed as p1.s1.v1), and results in anintermediary product. That intermediary product enters Stage 2 (104) andis acted on with operating condition 1 and operating condition 2 in thatstage (expressed as p1.s2.v1 and p1.s2.v2). Stage 2 produces a secondintermediary product. That second intermediary product enters Stage 3(106) is acted on with one or more operating conditions, resulting in athird intermediary product, and so forth. The third intermediary productenters Stage 4 (108) and is processed at Stage 4, under the operatingconditions there, and the end product, Product 1 is produced. Thedifferent operating conditions at the stages (e.g., 102, 104, 106, 108)of the manufacturing affect how the end product is manufactured and thequality of the end product.

In one embodiment, each of the area of the manufacturing stages (102,104, 106, 108) is equipped with sensors or like devices that measure theoperating conditions in real-time. The sensors, for example, are IoTsensors or devices. The sensors are connected (e.g., wirelessly orwired) to an IoT device connectivity, message platform 110, a hardwareplatform that may control connection and collection of data from IoTsensors. An example of such platform may include the IoT Foundation™from International Business Machines Corporation (IBM®), in Armonk, N.Y.A protocol such as the Message Query Telemetry Transport (MQTT) protocolmay be used to communicate or receive sensor detected data from themanufacturing stages (102, 104, 106, 108). Examples of IoT sensors mayinclude, but are not limited to, thermocouple (which measurestemperature), pressure gauge, material flow rate meter, and luminancemeter. For instance, via the IoT device connectivity, message platform110, the IoT sensors at the stages may be controlled to detect andtransmit sensor data at specified time intervals. The IoT deviceconnectivity, message platform 110 can specify or map which sensors atwhich stages the measured data should be sent at what time interval. Inone embodiment, the measured (also called detected) and transmittedsensor data is real time data that the sensors at respective stagesdetected at the specified time interval.

The area of the stages (or equipment, processing unit or containerwithin which the material is processed) are also connected to acontroller, an advisory system, or one or more actuators 112 (e.g.,referred to as a controller) that automatically control, actuate orimplement an operating condition under which the material orintermediary product is operated. The controller 112 may send signals tothe processing equipment or unit to set the operating conditions of thestages or in the area of the stages.

Referring to FIG. 1B, at 114, sensor data from the manufacturing stagesare received via the IoT device connectivity, message platform 110. Forinstance, a database system at 114 may receive and store real time datafrom sensors or devices (e.g., all sensors) at stages (e.g., all stages)of a manufacturing process. The database system at 114 may store thereal time data in the format of product, stage, operating conditiontuple as described above. In addition, the real time data also includethe end product quality data, for example, which may be measured by aninstrument that measures the product quality such as the surfacesmoothness, purity, carbon content, strength (e.g., of steel in steelmanufacturing). The database system at 114 may transmit the real timedata to a historian database system 116. In one embodiment, the databasesystem at 114 may store or keep real time data of a defined period, forexample, most recent past 1 hour or 1 day or another time periodreceived from the IoT device connectivity, message platform 110.

At 116, history data is processed. For example, the historian databasesystem accumulates measurement data with time stamp for each measurementdata received from the database system at 114, and stores themeasurement data as historical data, for example, for long term storage.

At 118, product genealogy data is generated or retrieved. Productgenealogy data specifies the stages and operating conditions a productwas processed through and the product qualify produced at the end of thestages.

At 120, on-line, automatic learning of control rules takes place, forexample, by executing a machine learning algorithm. Inputs to themachine learning algorithm include the product genealogy data andfeatures (processing conditions or operating conditions) that areselected for training a machine learning model. An example of a machinelearning algorithm is a decision tree algorithm. Example of a learnedrule is shown at 136, which includes “If p1.s1.v1>a & p1.s2.v1<b &p1.s3.v1>c Then p1.q1>d.” Example outputs of the machine learningalgorithm are shown in FIG. 1C-1, FIG. 1C-2, and FIG. 1C-3. The machinelearning model that is learned is run to produce control rules. Amachine learning model has a mathematical expression of predicting aresponse variable (also called dependent variable) in terms of statevariables (also called independent variables) and coefficients for statevariables. The learning of machine learning model is to compute thecoefficients using the historic data (also called trained data).

At 122, the control rules output by the machine learning model areranked by their effectiveness. Example effectiveness measures are shownat 138. For instance, the effectiveness of the control rules aremeasured by the quality of the product that is produced when using thoserules. The effectiveness of a control rule may be determined byproducing a product via the manufacturing stages, applying the controlrule during the manufacturing stages of the product, and measuring thequality of the end product produced based on applying the control rule.In one aspect, one or more control rules may be applied during themanufacturing of the product, and the effectiveness may be measured forthe one or more control rules together based on the end product producedby applying those one or more control rules.

An example computation for determining the effectiveness of controlrule(s) may include:E _(i) =E _(i,0)+(1−E_(i,0))×(Defect_(avg)−Defect_(i))/Defect_(avg),(for rule i)whereE_(i)=new effectiveness of rule i,E_(i,0)=effectiveness of rule i before the rule is applied at currenttime=0.5 (initial value, arbitrary).Defect_(avg)=average of defect index before the rule i is applied atcurrent time,Defect_(i)=defect index after the rule i is applied at current time,

An example of threshold for rule firing may be 0.7. That is, only ruleswith effectiveness higher than 0.7 may be selected for firing. Thoserules with effectiveness of less than 0.7 may not fire even if theif-part of the rule is satisfied with respect to the real time sensordata and product genealogy data. Briefly, a rule firing refers toexecuting or activating the THEN part of a rule responsive to thecriteria in the conditional part of the rule (IF part) being met.

At 124, based on the ranking produced at 122, n-number of control ruleshaving most effectiveness is selected. The control rules, for example,may include one or more feedback rules. The control rules may alsoinclude one or more feed forward rules. An example of a feedback ruleincludes a rule that sets an operating condition of a current stage. Anexample of a feed forward rule includes a rule that sets an operatingcondition of a future stage. For instance, consider for producingproduct 1, that an intermediary product is being processed at anintermediate stage, e.g., S2 (104); consider also that the operatingcondition(s) meets a conditional part of a control rule. A feedback rulesuggests setting an operating condition at that intermediate stage,e.g., S2 (104). An example of a feedback rule, for example, for stage 2,may specify: If p1.s1.v1>a and p1.s2.v1<b, then set p1.s2.v1=c, where aand b are values for operating condition or feature v1. For instance, ifproduct 1 (or raw or intermediary material for producing product 1)encountered operating condition v1>a at stage 1 and v1<b at stage 2,then the conditional part of this rule is met, and the rules indicatesthat v1 should be set to value c at stage 2. A feed forward rulesuggests setting an operating condition at a later or future stage thatfollows the current stage. An example of a feed forward rule, forexample, at stage 2, may specify: If p1.s1.v1>a and p1.s2.v1<b, then setp1.s3.v1=d. This feed forward rule specifies that if an operatingcondition or feature v1 at stage S1 is greater than value a, and anoperating condition or feature v1 at stage S2 is less than value b, thenan operating condition or feature v1 at stage S3 should be set to valued. Those example feedback and feed forward control rules are shown at126 and 128. A controller 112 actuating an operating condition at stage2 based on a feedback control rule is shown at 132 in FIG. 1A. Acontroller 112 actuating an operating condition at stage 2 based on afeed forward control rule is shown at 134 in FIG. 1A.

Feed forward analytics provide mitigation settings (for example, setpoints or recipes) for downstream processing steps based on processingconditions of upstream processing steps. A feed forward rule may beapplicable when the control action has to be ordered in advance ofexecution, for example, with a positive lead time. For instance, acontrol execution occurs after a time delay after the action is ordered.An example feed forward rule may include the following:

IF ContinuousCasting.Status=“Done” and ContinuousCasting.Temperature<30

-   -   i. and HotRolling.BilletSpeed.Status=“Done” and        HotRolling.BilletSpeed<18.0    -   ii. and ColdRolling.Status=“Unstart”        THEN action (REST, URL, (ColdRolling.Backload=180)@HTTP Header)

Feedback Analytics provide mitigation settings (for example, set pointsor recipes) for current processing steps based on processing conditionsof upstream processing steps. A feedback rule may be applicable onlywhen the control action can be carried out instantly at the currentprocessing step. For instance, a control execution occurs without anytime delay when the action is ordered.

At 130, the control rules selected at 124 are instantiated with realtime sensor detected data. For instance, the real-time manufacturingoperating condition data received and stored in the database at 114 arefed into the control rules, and one or more of the control rules arefired or executed if the real-time manufacturing operating conditiondata meet the conditional part of the control rules. Responsive to thefiring of a control rule (meeting the IF clause of the rule causes theTHEN part of the rule to activate), a controller or an actuator at 112is engaged. The controller or the actuator at 112 automatically controlsor actuates the operating setting suggested in the THEN part of therule. For instance, if the THEN part of the rule that is fired specifiesthat a temperature (operating condition or feature) should be set to Xdegrees Kelvin at stage 2, the controller (or actuator) automaticallychanges the thermostat setting of stage 2 in the manufacturing processto X degrees.

FIG. 1C-1, FIG. 1C-2, and FIG. 1C-3 show machine learning and outputs ofthe machine learning. The figures at 140 show a list of processvariables with an order of importance. A subset of variables with highvalues of importance numbers are chosen to develop a decision treemodel. The figure at 142 and 144 show two different illustrations ofexample resulting decision trees in graphical format. The figure at 146shows a text representation of a resulting decision tree.

In one embodiment, the processing shown at FIG. 1A and the processingshown at FIG. 1B may be performed in parallel, for instance,continually. For instance, the learning and generation of control rulesmay occur periodically with additional real time data received from theIoT connectivity messaging platform.

FIG. 2 is a flow diagram illustrating a method of the present disclosurein one embodiment. At 202, an IoT device connectivity messaging platformis configured to receive manufacturing process data from sensors locatedat different stages in multi-stage manufacturing process. For example,in multi-stage manufacturing, different stages or stage areas are setfor processing material to produce a product. Each stage area may beequipped with sensors and devices that set and detect operatingconditions at the stage. For example, a manufacturing facility may haveprocessing units that process material or intermediary product toproduce a final or end product. A stage for example corresponds to aprocessing unit. Each processing unit may have one or more sensorscoupled to the processing unit and measure process variables. Examplesof processing units may include blast furnace, hot rolling and coldrolling in a steel manufacturing process. Examples of sensors coupled toa processing unit may include thermocouple, pressure measurement gauge,and flow rate meter.

The IoT device connectivity message platform is configured tocommunicate with those sensors at all stages to receive real-timeoperating condition data of a stage when a product is being processed atthat stage, for all stages and all operating conditions, and for allproducts that are manufactured by the multi-stage manufacturing process.For instance, the IoT connectivity messaging platform is configured tocontrol and receive the real time sensor data from the sensors at everyspecified interval of time, for example, continuously.

At 204, a database system receives the manufacturing process data andbuilds a database comprising a tuple comprising product, stage, andprocessing variable. A processing variable represents an operatingcondition such as temperature, pressure, flow rate, chemical content,and/or others. The operating conditions may pertain to the product(material of the product) itself and also to a condition within aprocessing unit (for example, temperature of the product materialitself, or for example, temperature inside a processing unit. Thedatabase also includes product quality data, for example, represented asa tuple comprising a product identifier and quality measure of theproduct identified by the product identifier.

At 206, product genealogy data is generated based on the database ofreal time sensor data and product quality data. The product genealogydata specifies a product's history of its manufacturing process(processing conditions), for example, what stages or processing units aproduct went through to get manufactured, the operating conditions ofthose stages (process variables), and the product's quality.

At 208, a number of process variables (operating or processingconditions) of production stages that influence the product quality areselected. The process variables may be selected from the processvariables specified in the product genealogy data. Examples of processvariables may include temperature, pressure, flow rate, and chemicalcontent. The selection may be based on expert knowledge. In anotheraspect, machine learning for feature selection may be employed todetermine which process variables should be selected (as shown in 140).

At 210, the selected process variables and the genealogy data (e.g.,received) are input to a machine learning algorithm such as a decisiontree algorithm. The decision tree algorithm learns causal relationshipsbetween the process variables (processing or operating conditions) ofall production stages and the product quality measure. Decision tree isa type of machine learning model which includes a mathematicalexpression of predicting a response variable (also called dependentvariable) in terms of state variables (also called independentvariables) and coefficients for state variables. The learning of machinelearning model (for example decision trees) includes computing thecoefficients using the historic processing data (also called trainingdata or historian data).

Based on the learned relationships, control rules are automaticallygenerated. The generated control rules may include feedback rules andfeed forward rules. For example, each branch of a decision tree can becaptured as a control rule as shown in 146. A computer by executingcomputer instructions to separate a branch as a control rule, mayautomatically generate control rules based on the branches of a decisiontree that is learned by a machine autonomously.

The automatically generated control rules provide processing set points(values for operating or processing conditions for one or moreproduction or manufacturing stages) for one or more stages in themulti-stage manufacturing process so that the desired product quality isobtained. The control rules may be also referred to as quality controlrules.

At 212, the automatically generated quality control rules may be furtherfiltered to include a subset that are determined to be most effective inimproving the end product quality. This selection or filtering may beperformed by applying those rules during a multi-stage manufacturingprocess and evaluating or measuring the end product's quality. Then-number of control rules that were applied in producing products withhigher product quality is selected. In one aspect, the control rulesthat are applied during a manufacturing process that produced a producthaving a quality value that exceeds a defined threshold value, may beselected. For example, the control rules that are generated are rankedand a subset of the control rules that are top ranked according to adefined threshold is selected.

At 214, the subset of the quality control rules is instantiated. Thesubset includes those control rules determined to be (or filtered at212) most effective in producing a quality product (e.g., those thathave a quality measure that exceeds a threshold quality value).Instantiation, for example, includes feeding into those selected controlrules, the real time data that is being received at the IoT connectivitymessaging platform, for example, and processed in the database.Instantiation of control rules may also include feeding into the rulesthe historical product genealogy data, for example, for those rules thattest past processing stage data. When the real time sensor data (ande.g., the historical product genealogy data, depending on theconditional part of the control rule) meet the control rule'sconditional part, the control rule will fire, signaling to control aprocess variable (processing condition) set point at a stage. An exampleof a control rules may include, for example, at stage 2, if p1.s1.v1>aand if p1.s2.v1<b for defined values a and b, then p1.s2.v1=c. So, forexample, if product 1 is being processed at stage 2, and its processingvariable v1 at stage 1 had a value greater than a, and the processingvariable v1 of product 1 currently at stage 2 is less than value b, thecontrol rule files, signaling to set or control the processing variablev1 of product 1 at stage 2 to value c.

At 216, responsive to the firing of the control rule, a control actionto set the processing variable to a set point determined or specified bythe rule is sent or transmitted to a controller, which controls a sensoror device at the corresponding stage or coupled to the processing unitto physically set the processing variable (also referred to an operatingcondition) to the value determined by (specified in) the control rulethat fired. For example, the control actions (feedback and/or feedforward rule or rules) are sent to a process controller, processadvisory system and/or actuator to implement the control action.

At 218, the effect of the control actions are stored as productgenealogy data in a product genealogy database and the effectiveness ofthe control rules are recorded.

FIG. 3 shows an example user interface display showing productmanufacturing process with instantiation of control rules based on realtime IoT sensor data that may actuate changing or controlling one ormore set points, in one embodiment of the present disclosure. A displaypanel at 302 shows six stages in multi-stage manufacturing process. Inthis example, stages include raw material processing at a raw materialprocessing unit, processing at blast furnace, processing at oxygenfurnace, continuous casting at another casting unit, hot rolling at hotrolling unit, and cold rolling at cold rolling unit. Historical defectindex is shown at 304 and shows defect information (product quality)input to a machine learning algorithm to learn causal relationshipsbetween process variables (processing conditions) and defects (productquality) in a product. Rules that are generated based on the causalrelationships are shown at 306. The display panel at 308 shows real timedefect index (product qualify information) that results after applyingone or more control rules, which automatically causes actuation of setpoints in one or more stages (processing units) in real time. Thedisplay panel at 310 shows the control actions (actuations) that areautomatically taken, based on applying the control rules in real time.The display panel at 312 shows log data.

In one embodiment, the automated or autonomous learning of the controlrules are performed on-line and the control rules are integrated intothe process control of manufacturing process. The on-line rules areautomatically or autonomously adjusted based on their effectiveness, forexample, those that have effectiveness that exceed a defined thresholdeffectiveness may be selected for instantiation. The control rules maybe instantiated in real time based on both real time data (e.g., realtime IoT sensor data) and historic product genealogy data.

FIG. 4 is a diagram illustrating system components in one embodiment ofthe present disclosure. A hardware processor 402 is coupled to a storagedevice 404. The storage device 404 stores product genealogy data, forexample, in a database system or via another repository mechanism. Theproduct genealogy data specifies history of processing conditions at allstages (e.g., at 412 a, 412 b, 412 c, 412 n) in the multi-stagemanufacturing process in manufacturing a product, for example, formultiple products. The processing conditions may be expressed and storedin terms of process variables and corresponding values measured bysensors. The sensors (e.g., 414, 416, 418, 420, 422, 424, 426 . . . )are coupled to processing units (e.g., 412 a, 412 b, 412 c, 412 n) in amanufacturing facility 410 implementing the multi-stage manufacturingprocess, the hardware processor 402 receiving the product genealogydata. The genealogy data also include the product quality data. Thehardware processor 402 selects a subset of process variables thatinfluence product quality, from the process variables, and the hardwareprocessor 402 executes a machine learning algorithm with the productgenealogy data and the subset of process variables as input. The machinelearning algorithm learns causal relationships between the processingconditions and the product quality. Based on the learned causalrelationships, the hardware processor automatically generates controlrules for controlling process variable set points at one or more stages(e.g., at 412 a, 412 b, 412 c, 412 n) in the multi-stage manufacturingprocess, for example, to improve the quality of a product beingmanufactured. The hardware processor 402 may receive real time sensordata transmitted by the sensors and instantiates one or more of thecontrol rules based on the real time sensor data. Responsive to the realtime sensor data meeting a conditional part of a control rule, a controlrule is fired. Responsive to the control rule firing, the hardwareprocessor 402 controls (for example, sends a signal to control) anactuator 408 coupled to one or more of the processing units (e.g., 412a, 412 b, 412 c, 412 n) to set a processing variable to a set pointspecified in the control rule.

In one embodiment, the hardware processor 402 configures an IoTconnectivity messaging platform 406 to control and receive the real timesensor data from the sensors (e.g., 412 a, 412 b, 412 c, 412 n) at everyspecified interval of time continuously. In one embodiment, the IoTplatform 406 may be cloud-based platform.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a manufacturing process control system in oneembodiment of the present disclosure. The computer system is only oneexample of a suitable processing system and is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe methodology described herein. The processing system shown may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with the processing system shown in FIG. 5 may include,but are not limited to, personal computer systems, server computersystems, thin clients, thick clients, handheld or laptop devices,multiprocessor systems, microprocessor-based systems, set top boxes,programmable consumer electronics, network PCs, minicomputer systems,mainframe computer systems, and distributed cloud computing environmentsthat include any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

We claim:
 1. A computer program product for controlling productproduction in multi-stage manufacturing process, the computer programproduct comprising a computer readable storage medium having programinstructions embodied therewith, wherein the computer readable storagemedium is not a transitory signal per se, the program instructionsexecutable by a processor to cause the processor to perform a methodcomprising: receiving product genealogy data comprising at least ahistory of processing conditions at stages in the multi-stagemanufacturing process in manufacturing a product, the processingconditions expressed in terms of process variables and correspondingvalues measured by sensors coupled to processing units in amanufacturing facility implementing the multi-stage manufacturingprocess; selecting a subset of process variables that influence productquality, from the process variables; executing a machine learningalgorithm with the product genealogy data and the subset of processvariables as input, the machine learning algorithm learning causalrelationships between the processing conditions and the product quality;automatically generating a control rule based on at least one of thecausal relationships, the control rule for controlling a processvariable set point at a stage in the multi-stage manufacturing process;receiving real time sensor data from the sensors; instantiating thecontrol rule based on the real time sensor data, wherein the controlrule is fired responsive to the real time sensor data meeting aconditional part of the control rule; responsive to the control rulefiring, controlling an actuator coupled to at least one of theprocessing units to set a processing variable to a set point specifiedin the control rule; wherein the control rule comprises at least a rulethat sets an operating condition at a future manufacturing process stagethat follows a current manufacturing process stage.
 2. The computerprogram product of claim 1, wherein the control rule comprises aplurality of control rules, the method further comprising ranking thecontrol rules that are generated and selecting a subset of the controlrules that are top ranked according to a defined threshold, wherein thecontrol rule that is instantiated is in the top ranked subset.
 3. Thecomputer program product of claim 1, further comprising: configuring anInternet of Things (IoT) connectivity messaging platform to control andreceive the real time sensor data from the sensors at every specifiedinterval of time continuously.
 4. The computer program product of claim3, wherein the product genealogy data is built based on the real timedata received from the IoT connectivity messaging platform.
 5. Thecomputer program product of claim 1, wherein the control rule comprisesat least a feedback rule.
 6. The computer program product of claim 1,wherein the control rule comprises at least a feed forward rule.
 7. Asystem of controlling product production in multi-stage manufacturingprocess, comprising: a hardware processor communicatively coupled to astorage device storing product genealogy data, the product genealogydata comprising at least a history of processing conditions at allstages in the multi-stage manufacturing process in manufacturing aproduct, the processing conditions expressed in terms of processvariables and corresponding values measured by sensors coupled toprocessing units in a manufacturing facility implementing themulti-stage manufacturing process, the hardware processor receiving theproduct genealogy data, the hardware processor selecting a subset ofprocess variables that influence product quality, from the processvariables, the hardware processor executing a machine learning algorithmwith the product genealogy data and the subset of process variables asinput, the machine learning algorithm learning causal relationshipsbetween the processing conditions and the product quality, the hardwareprocessor automatically generating a control rule based on at least oneof the causal relationships, the control rule for controlling a processvariable set point at a stage in the multi-stage manufacturing process,the hardware processor receiving real time sensor data from the sensorsand instantiating the control rule based on the real time sensor data,wherein the control rule is fired responsive to the real time sensordata meeting a conditional part of the control rule, responsive to thecontrol rule firing, the hardware processor controlling an actuatorcoupled to at least one of the processing units to set a processingvariable to a set point specified in the control rule, wherein thecontrol rule comprises at least a rule that sets an operating conditionat a future manufacturing process stage that follows a currentmanufacturing process stage.
 8. The system of claim 7, wherein thecontrol rule comprises a plurality of control rules, wherein thehardware processor further ranks the control rules based on theireffectiveness and selecting a subset of the control rules that are topranked according to a defined threshold, wherein the control rule thatis instantiated is in the top ranked subset.
 9. The system of claim 7,wherein the hardware processor further configures an Internet of Things(IoT) connectivity messaging platform to control and receive the realtime sensor data from the sensors at every specified interval of timecontinuously.
 10. The system of claim 9, wherein the product genealogydata is built based on the real time data received from the IoTconnectivity messaging platform.
 11. The system of claim 7, wherein thecontrol rule comprises at least a feedback rule.
 12. The system of claim7, wherein the control rule comprises at least a feed forward rule. 13.The system of claim 7, wherein the control rule comprises at least arule that sets an operating condition of a current manufacturing processstage.
 14. The computer program product of claim 1, wherein the controlrule comprises at least a rule that sets an operating condition of acurrent manufacturing process stage.